China’s Impact on Global Financial Markets
Bibliographic record
Abstract
We analyze shifts in the structure of China's capital outflows over the past decade.The composition of gross outflows has shifted from accumulation of foreign exchange reserves by the central bank to nonofficial outflows.Unlocking the enormous pool of domestic savings could have a significant impact on global financial markets as China continues to open up its capital account and as domestic investors look abroad for returns and diversification.We analyze in detail the allocation patterns of Chinese institutional investors (IIs), which constitute the main channel for foreign portfolio investment outflows.We find that, relative to benchmarks based on market capitalization, Chinese IIs underweight developed countries and high-tech sectors in their international portfolio allocations but overinvest in high-tech stocks in developed countries.To further examine Chinese IIs' joint decisions on destination country-sector pairs, we construct continuous measures of revealed relative comparative advantage and disadvantage in a sector for a country based on trade patterns.We find that, in their foreign portfolio allocations, Chinese IIs overweight sectors in which China has a comparative disadvantage.Moreover, Chinese IIs concentrate such investments in countries that have higher relative comparative advantage in those sectors.Diversification and information advantages related to foreign imports to China seem to influence patterns of foreign portfolio allocations, while yield-seeking and learning motives do not.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".